We compare MSTR with state-of-the-art methods in Table REF  and Table REF .
In Table REF , MSTR outperforms the previous state-of-the-art method in V-COCO dataset by a large margin (+3.2p in \(\text{AP}_{\text{role}}^{\#1}\)  and +4.2p in \(\text{AP}_{\text{role}}^{\#1}\) ).
Similar to this, in Table REF , MSTR achieves the highest mAP on HICO-DET dataset in all Full, Rare, and Non-Rare classes obtaining +2.1p, +3.46p, and +1.69p gain for each compared to the previous state-of-the-art.
We use the same scoring function as QPIC without any modification for a fair comparison.
Note that MSTR benefits from the advantages of using deformable attention: the fast convergence for training [1]} (see more details and the convergence graph in our Appendix).
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